Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow as memory model.
- Lazy | eager execution
- Multi-threaded
- SIMD
- Query optimization
- Powerful expression API
- Rust | Python | ...
To learn more, read the User Guide.
You can take latest release from crates.io
, or if you want to use the latest features/ performance improvements
point to the master
branch of this repo.
You can add the master like this:
polars = {version="0.13.0", git = "https://github.com/ritchie46/polars" }
Or by fixing to a specific version:
polars = {version="0.13.0", git = "https://github.com/ritchie46/polars", rev = "<optional git tag>" }
Required Rust version >=1.51
Polars is currently transitioning from py-polars
to polars
. Some docs may still refer the old name.
Install the latest polars version with:
$ pip3 install polars
Want to know about all the features Polars support? Read the docs!
- installation guide:
$ pip3 install polars
- User Guide
- Reference guide
Polars is written to be performant, and it is! But don't take my word for it, take a look at the results in h2oai's db-benchmark.
Additional cargo features:
temporal (default)
- Conversions between Chrono and Polars for temporal data
simd (nightly)
- SIMD operations
parquet
- Read Apache Parquet format
json
- Json serialization
ipc
- Arrow's IPC format serialization
random
- Generate array's with randomly sampled values
ndarray
- Convert from
DataFrame
tondarray
- Convert from
lazy
- Lazy api
strings
- String utilities for
Utf8Chunked
- String utilities for
object
- Support for generic ChunkedArray's called
ObjectChunked<T>
(generic overT
). These will downcastable from Series through the Any trait.
- Support for generic ChunkedArray's called
[plain_fmt | pretty_fmt]
(mutually exclusive)- one of them should be chosen to fmt DataFrames.
pretty_fmt
can deal with overflowing cells and looks nicer but has more dependencies.plain_fmt (default)
is plain formatting.
- one of them should be chosen to fmt DataFrames.
Want to contribute? Read our contribution guideline.
POLARS_PAR_SORT_BOUND
-> Sets the lower bound of rows at which Polars will use a parallel sorting algorithm. Default is 1M rows.POLARS_FMT_MAX_COLS
-> maximum number of columns shown when formatting DataFrames.POLARS_FMT_MAX_ROWS
-> maximum number of rows shown when formatting DataFrames.POLARS_TABLE_WIDTH
-> width of the tables used during DataFrame formatting.POLARS_MAX_THREADS
-> maximum number of threads used in join algorithm. Default is unbounded.POLARS_VERBOSE
-> print logging info to stderr
If you want a bleeding edge release or maximal performance you should compile py-polars from source.
This can be done by going through the following steps in sequence:
- install the latest rust compiler
$ pip3 install maturin
- Choose any of:
- Very long compile times, fastest binary:
$ cd py-polars && maturin develop --rustc-extra-args="-C target-cpu=native" --release
- Shorter compile times, fast binary:
$ cd py-polars && maturin develop --rustc-extra-args="-C codegen-units=16 lto=no target-cpu=native" --release
Note that the Rust crate implementing the Python bindings is called py-polars
to distinguish from the wrapped
Rust crate polars
itself. However, both the Python package and the Python module are named polars
, so you
can pip install polars
and import polars
(previously, these were called py-polars
and pypolars
).
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